Overview

Dataset statistics

Number of variables24
Number of observations3079
Missing cells4385
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory577.4 KiB
Average record size in memory192.0 B

Variable types

CAT11
NUM10
BOOL3

Warnings

VISIT_ID has constant value "3079" Constant
VISIT_ID.1 has constant value "3079" Constant
ROW_NO has constant value "3079" Constant
BARCODE2_V2 has a high cardinality: 459 distinct values High cardinality
DTBLDR Date of blood draw has a high cardinality: 1064 distinct values High cardinality
ETHFM - Father's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know is highly correlated with ETHFF - Father's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowHigh correlation
ETHFF - Father's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know is highly correlated with ETHFM - Father's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowHigh correlation
ETHFM - Father's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know is highly correlated with ETHFF - Father's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowHigh correlation
ETHFF - Father's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know is highly correlated with ETHFM - Father's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowHigh correlation
ETHMM - Mother's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know is highly correlated with ETHMF - Mother's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowHigh correlation
ETHMF - Mother's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know is highly correlated with ETHMM - Mother's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowHigh correlation
BARCODE2_V2 has 2620 (85.1%) missing values Missing
DISEASE2 1=MS 2=NMO 3=ON 4=TM 5=ADEM 6=CIS blank = control has 704 (22.9%) missing values Missing
ETHYOU - Participant ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know has 39 (1.3%) missing values Missing
ETHFATH - Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know has 82 (2.7%) missing values Missing
ETHMOTH - Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know has 81 (2.6%) missing values Missing
ETHFF - Father's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know has 86 (2.8%) missing values Missing
ETHFM - Father's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know has 86 (2.8%) missing values Missing
ETHMF - Mother's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know has 86 (2.8%) missing values Missing
ETHMM - Mother's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't know has 85 (2.8%) missing values Missing
RACFATH - Father's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't know has 80 (2.6%) missing values Missing
RACMOTH - Mother's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't know has 79 (2.6%) missing values Missing
RACFF - Father's Father's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't know has 84 (2.7%) missing values Missing
RACFM - Father's Mother's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't know has 84 (2.7%) missing values Missing
RACMF - Mother's Father's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't know has 84 (2.7%) missing values Missing
RACMM - Mother's Mother's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't know has 85 (2.8%) missing values Missing
BARCODE2_V2 is uniformly distributed Uniform
BARCODE2 has unique values Unique

Reproduction

Analysis started2022-11-09 15:22:49.125275
Analysis finished2022-11-09 15:23:20.384157
Duration31.26 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

BARCODE2
Categorical

UNIQUE

Distinct3079
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
AC000003
 
1
AC002138
 
1
AC002276
 
1
AC002277
 
1
AC002278
 
1
Other values (3074)
3074 
ValueCountFrequency (%) 
AC0000031< 0.1%
 
AC0021381< 0.1%
 
AC0022761< 0.1%
 
AC0022771< 0.1%
 
AC0022781< 0.1%
 
AC0022791< 0.1%
 
AC0022801< 0.1%
 
AC0022811< 0.1%
 
AC0022821< 0.1%
 
AC0022841< 0.1%
 
Other values (3069)306999.7%
 
2022-11-09T10:23:20.601528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3079 ?
Unique (%)100.0%
2022-11-09T10:23:20.786415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

BARCODE2_V2
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct459
Distinct (%)100.0%
Missing2620
Missing (%)85.1%
Memory size24.1 KiB
AL100611
 
1
AL100971
 
1
AL100523
 
1
AL100989
 
1
AL100808
 
1
Other values (454)
454 
ValueCountFrequency (%) 
AL1006111< 0.1%
 
AL1009711< 0.1%
 
AL1005231< 0.1%
 
AL1009891< 0.1%
 
AL1008081< 0.1%
 
AL1009561< 0.1%
 
AL1007701< 0.1%
 
AL1005021< 0.1%
 
AL1009061< 0.1%
 
AL1002361< 0.1%
 
Other values (449)44914.6%
 
(Missing)262085.1%
 
2022-11-09T10:23:20.987395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique459 ?
Unique (%)100.0%
2022-11-09T10:23:21.157929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length3
Mean length3.745371874
Min length3

SITE_ID
Real number (ℝ≥0)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.31276388
Minimum1
Maximum86
Zeros0
Zeros (%)0.0%
Memory size24.1 KiB
2022-11-09T10:23:21.305772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q178
median79
Q382
95-th percentile84
Maximum86
Range85
Interquartile range (IQR)4

Descriptive statistics

Standard deviation31.68955676
Coefficient of variation (CV)0.4927413293
Kurtosis0.2418446723
Mean64.31276388
Median Absolute Deviation (MAD)2
Skewness-1.489887519
Sum198019
Variance1004.228008
MonotocityNot monotonic
2022-11-09T10:23:21.447041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
7874624.2%
 
161520.0%
 
8260019.5%
 
7953717.4%
 
801916.2%
 
811835.9%
 
85632.0%
 
84571.9%
 
83451.5%
 
86421.4%
 
ValueCountFrequency (%) 
161520.0%
 
7874624.2%
 
7953717.4%
 
801916.2%
 
811835.9%
 
ValueCountFrequency (%) 
86421.4%
 
85632.0%
 
84571.9%
 
83451.5%
 
8260019.5%
 

PATIENT_ID
Real number (ℝ≥0)

Distinct952
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327.1646639
Minimum1
Maximum997
Zeros0
Zeros (%)0.0%
Memory size24.1 KiB
2022-11-09T10:23:21.642559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q1114
median295
Q3496
95-th percentile812.1
Maximum997
Range996
Interquartile range (IQR)382

Descriptive statistics

Standard deviation244.9938241
Coefficient of variation (CV)0.7488395025
Kurtosis-0.3835799867
Mean327.1646639
Median Absolute Deviation (MAD)190
Skewness0.6233073065
Sum1007340
Variance60021.97385
MonotocityNot monotonic
2022-11-09T10:23:21.944482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2190.3%
 
1990.3%
 
690.3%
 
3490.3%
 
890.3%
 
3190.3%
 
1690.3%
 
490.3%
 
590.3%
 
1790.3%
 
Other values (942)298997.1%
 
ValueCountFrequency (%) 
190.3%
 
290.3%
 
390.3%
 
490.3%
 
590.3%
 
ValueCountFrequency (%) 
9971< 0.1%
 
9951< 0.1%
 
9941< 0.1%
 
9931< 0.1%
 
9921< 0.1%
 

DTBLDR Date of blood draw
Categorical

HIGH CARDINALITY

Distinct1064
Distinct (%)34.6%
Missing7
Missing (%)0.2%
Memory size24.1 KiB
7/17/08
 
40
11/10/10
 
26
10/23/12
 
25
11/9/11
 
25
10/24/12
 
23
Other values (1059)
2933 
ValueCountFrequency (%) 
7/17/08401.3%
 
11/10/10260.8%
 
10/23/12250.8%
 
11/9/11250.8%
 
10/24/12230.7%
 
11/11/09150.5%
 
2/28/07120.4%
 
10/24/07120.4%
 
3/28/07110.4%
 
11/14/07110.4%
 
Other values (1054)287293.3%
 
2022-11-09T10:23:22.287407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique345 ?
Unique (%)11.2%
2022-11-09T10:23:22.567569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length6.991230919
Min length3
Distinct6
Distinct (%)0.3%
Missing704
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean1.598315789
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size24.1 KiB
2022-11-09T10:23:22.745418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.244836558
Coefficient of variation (CV)0.7788426829
Kurtosis4.402739134
Mean1.598315789
Median Absolute Deviation (MAD)0
Skewness2.296742701
Sum3796
Variance1.549618055
MonotocityNot monotonic
2022-11-09T10:23:22.933684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
1173656.4%
 
234111.1%
 
41665.4%
 
6862.8%
 
5301.0%
 
3160.5%
 
(Missing)70422.9%
 
ValueCountFrequency (%) 
1173656.4%
 
234111.1%
 
3160.5%
 
41665.4%
 
5301.0%
 
ValueCountFrequency (%) 
6862.8%
 
5301.0%
 
41665.4%
 
3160.5%
 
234111.1%
 
Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.1%
Memory size24.1 KiB
2
2375 
1
701 
ValueCountFrequency (%) 
2237577.1%
 
170122.8%
 
(Missing)30.1%
 
2022-11-09T10:23:23.235760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-11-09T10:23:23.383628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:23.516457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

VISIT_ID
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
3079 
ValueCountFrequency (%) 
03079100.0%
 
2022-11-09T10:23:23.671544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

VISIT_ID.1
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
3079 
ValueCountFrequency (%) 
03079100.0%
 
2022-11-09T10:23:23.725408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

ROW_NO
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
3079 
ValueCountFrequency (%) 
03079100.0%
 
2022-11-09T10:23:23.784383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct3
Distinct (%)0.1%
Missing39
Missing (%)1.3%
Memory size24.1 KiB
2
2864 
1
 
130
3
 
46
ValueCountFrequency (%) 
2286493.0%
 
11304.2%
 
3461.5%
 
(Missing)391.3%
 
2022-11-09T10:23:23.937532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-11-09T10:23:24.090649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:24.277549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3
Distinct3
Distinct (%)0.1%
Missing82
Missing (%)2.7%
Memory size24.1 KiB
2
2824 
1
 
112
3
 
61
ValueCountFrequency (%) 
2282491.7%
 
11123.6%
 
3612.0%
 
(Missing)822.7%
 
2022-11-09T10:23:24.439337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-11-09T10:23:24.548008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:24.657407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3
Distinct3
Distinct (%)0.1%
Missing81
Missing (%)2.6%
Memory size24.1 KiB
2
2834 
1
 
110
3
 
54
ValueCountFrequency (%) 
2283492.0%
 
11103.6%
 
3541.8%
 
(Missing)812.6%
 
2022-11-09T10:23:24.830805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-11-09T10:23:24.936340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:25.037971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3
Distinct3
Distinct (%)0.1%
Missing86
Missing (%)2.8%
Memory size24.1 KiB
2
2797 
1
 
110
3
 
86
ValueCountFrequency (%) 
2279790.8%
 
11103.6%
 
3862.8%
 
(Missing)862.8%
 
2022-11-09T10:23:25.186581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-11-09T10:23:25.290875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:25.391152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3
Distinct3
Distinct (%)0.1%
Missing86
Missing (%)2.8%
Memory size24.1 KiB
2
2812 
1
 
99
3
 
82
ValueCountFrequency (%) 
2281291.3%
 
1993.2%
 
3822.7%
 
(Missing)862.8%
 
2022-11-09T10:23:25.542194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-11-09T10:23:25.640790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:25.746202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3
Distinct3
Distinct (%)0.1%
Missing86
Missing (%)2.8%
Memory size24.1 KiB
2
2813 
1
 
99
3
 
81
ValueCountFrequency (%) 
2281391.4%
 
1993.2%
 
3812.6%
 
(Missing)862.8%
 
2022-11-09T10:23:25.894291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-11-09T10:23:26.008841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:26.112307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3
Distinct3
Distinct (%)0.1%
Missing85
Missing (%)2.8%
Memory size24.1 KiB
2
2817 
1
 
104
3
 
73
ValueCountFrequency (%) 
2281791.5%
 
11043.4%
 
3732.4%
 
(Missing)852.8%
 
2022-11-09T10:23:26.277326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-11-09T10:23:26.388379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:26.495618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3
Distinct8
Distinct (%)0.3%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean6.728901922
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size24.1 KiB
2022-11-09T10:23:26.633271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median7
Q37
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.856459161
Coefficient of variation (CV)0.1272806724
Kurtosis14.52398326
Mean6.728901922
Median Absolute Deviation (MAD)0
Skewness-3.521296235
Sum20651
Variance0.7335222945
MonotocityNot monotonic
2022-11-09T10:23:26.767707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
7269887.6%
 
52728.8%
 
4301.0%
 
8210.7%
 
2160.5%
 
3140.5%
 
1130.4%
 
650.2%
 
(Missing)100.3%
 
ValueCountFrequency (%) 
1130.4%
 
2160.5%
 
3140.5%
 
4301.0%
 
52728.8%
 
ValueCountFrequency (%) 
8210.7%
 
7269887.6%
 
650.2%
 
52728.8%
 
4301.0%
 
Distinct8
Distinct (%)0.3%
Missing80
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean6.718572858
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size24.1 KiB
2022-11-09T10:23:26.909734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median7
Q37
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9501370099
Coefficient of variation (CV)0.1414194696
Kurtosis14.88766626
Mean6.718572858
Median Absolute Deviation (MAD)0
Skewness-3.597205925
Sum20149
Variance0.9027603375
MonotocityNot monotonic
2022-11-09T10:23:27.034306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
7260784.7%
 
52498.1%
 
8511.7%
 
4270.9%
 
1250.8%
 
2210.7%
 
3140.5%
 
650.2%
 
(Missing)802.6%
 
ValueCountFrequency (%) 
1250.8%
 
2210.7%
 
3140.5%
 
4270.9%
 
52498.1%
 
ValueCountFrequency (%) 
8511.7%
 
7260784.7%
 
650.2%
 
52498.1%
 
4270.9%
 
Distinct8
Distinct (%)0.3%
Missing79
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean6.740666667
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size24.1 KiB
2022-11-09T10:23:27.167887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median7
Q37
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.873880681
Coefficient of variation (CV)0.1296430641
Kurtosis14.91233829
Mean6.740666667
Median Absolute Deviation (MAD)0
Skewness-3.554826906
Sum20222
Variance0.7636674447
MonotocityNot monotonic
2022-11-09T10:23:27.310089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
7262785.3%
 
52518.2%
 
8441.4%
 
4301.0%
 
2190.6%
 
1140.5%
 
3120.4%
 
630.1%
 
(Missing)792.6%
 
ValueCountFrequency (%) 
1140.5%
 
2190.6%
 
3120.4%
 
4301.0%
 
52518.2%
 
ValueCountFrequency (%) 
8441.4%
 
7262785.3%
 
630.1%
 
52518.2%
 
4301.0%
 
Distinct8
Distinct (%)0.3%
Missing84
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean6.733222037
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size24.1 KiB
2022-11-09T10:23:27.440804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median7
Q37
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9742586047
Coefficient of variation (CV)0.1446942637
Kurtosis15.02698061
Mean6.733222037
Median Absolute Deviation (MAD)0
Skewness-3.594606642
Sum20166
Variance0.9491798288
MonotocityNot monotonic
2022-11-09T10:23:27.569048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
7257783.7%
 
52357.6%
 
8882.9%
 
1301.0%
 
4270.9%
 
2190.6%
 
3140.5%
 
650.2%
 
(Missing)842.7%
 
ValueCountFrequency (%) 
1301.0%
 
2190.6%
 
3140.5%
 
4270.9%
 
52357.6%
 
ValueCountFrequency (%) 
8882.9%
 
7257783.7%
 
650.2%
 
52357.6%
 
4270.9%
 
Distinct8
Distinct (%)0.3%
Missing84
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean6.699833055
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size24.1 KiB
2022-11-09T10:23:27.714137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median7
Q37
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.062689319
Coefficient of variation (CV)0.1586142984
Kurtosis13.87025424
Mean6.699833055
Median Absolute Deviation (MAD)0
Skewness-3.53391468
Sum20066
Variance1.129308589
MonotocityNot monotonic
2022-11-09T10:23:27.845471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
7256183.2%
 
52377.7%
 
8882.9%
 
1451.5%
 
4260.8%
 
2220.7%
 
3130.4%
 
630.1%
 
(Missing)842.7%
 
ValueCountFrequency (%) 
1451.5%
 
2220.7%
 
3130.4%
 
4260.8%
 
52377.7%
 
ValueCountFrequency (%) 
8882.9%
 
7256183.2%
 
630.1%
 
52377.7%
 
4260.8%
 
Distinct8
Distinct (%)0.3%
Missing84
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean6.749248748
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size24.1 KiB
2022-11-09T10:23:28.301325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median7
Q37
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9080442493
Coefficient of variation (CV)0.1345400478
Kurtosis14.48166349
Mean6.749248748
Median Absolute Deviation (MAD)0
Skewness-3.508068678
Sum20214
Variance0.8245443586
MonotocityNot monotonic
2022-11-09T10:23:28.435287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
7259684.3%
 
52337.6%
 
8782.5%
 
4311.0%
 
2240.8%
 
1150.5%
 
3140.5%
 
640.1%
 
(Missing)842.7%
 
ValueCountFrequency (%) 
1150.5%
 
2240.8%
 
3140.5%
 
4311.0%
 
52337.6%
 
ValueCountFrequency (%) 
8782.5%
 
7259684.3%
 
640.1%
 
52337.6%
 
4311.0%
 
Distinct8
Distinct (%)0.3%
Missing85
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean6.739478958
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size24.1 KiB
2022-11-09T10:23:28.583649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median7
Q37
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9375404813
Coefficient of variation (CV)0.1391117158
Kurtosis15.72619682
Mean6.739478958
Median Absolute Deviation (MAD)0
Skewness-3.652558984
Sum20178
Variance0.8789821541
MonotocityNot monotonic
2022-11-09T10:23:28.709972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
7259684.3%
 
52407.8%
 
8732.4%
 
4280.9%
 
1260.8%
 
2180.6%
 
3100.3%
 
630.1%
 
(Missing)852.8%
 
ValueCountFrequency (%) 
1260.8%
 
2180.6%
 
3100.3%
 
4280.9%
 
52407.8%
 
ValueCountFrequency (%) 
8732.4%
 
7259684.3%
 
630.1%
 
52407.8%
 
4280.9%
 

Interactions

2022-11-09T10:22:50.836108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:50.984616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:51.133842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:51.277615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:51.419555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:51.562228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:51.715516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:51.857986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:52.000750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:52.133488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:52.280469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:52.400096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:52.524524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:52.687200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:52.831157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:52.977373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:53.117329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:53.278370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:53.471108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:53.621704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:53.781918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:53.913670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:54.044540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:54.239888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:54.513822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:54.753490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:54.983758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:55.222248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:55.440055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:55.632215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:55.818588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:56.057355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:56.228662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:56.407351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:56.667607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:57.610223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:57.848266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:58.137959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:58.353472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:58.590369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:58.826441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:59.088773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:59.281145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:59.525598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:22:59.760741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:00.026665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:00.232659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:00.426946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:00.676332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:01.016559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:01.424862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:01.696543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:01.947539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:02.272900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:02.606247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:03.011335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:03.278205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:03.515000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:03.748756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:03.962460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:04.192218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:04.372954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:04.559179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:04.744963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:04.968054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:05.174081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:05.377788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:05.576728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:05.771616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:06.160320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:06.425139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:06.645580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:06.876718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:07.124933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:07.404993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:07.724766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:07.960307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:08.160783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:08.364911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:08.565987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:08.775657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:08.945515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:09.122289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:09.304774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:09.498590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:09.725178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:09.951502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:10.233152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:10.478115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:10.762833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:11.057622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:11.355610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:11.584135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:11.867205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:12.114328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:12.367985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:12.601010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:13.153158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:13.538055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:13.793693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-09T10:23:28.886558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-09T10:23:29.748343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-09T10:23:30.650426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-09T10:23:31.487704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-11-09T10:23:32.257019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-11-09T10:23:14.298982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:16.063644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:17.878882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-09T10:23:19.773338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

BARCODE2BARCODE2_V2SITE_IDPATIENT_IDDTBLDR Date of blood drawDISEASE2 1=MS 2=NMO 3=ON 4=TM 5=ADEM 6=CIS blank = controlDIAGDIS2 1=Control 2=CaseVISIT_IDVISIT_ID.1ROW_NOETHYOU - Participant ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHFATH - Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHMOTH - Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHFF - Father's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHFM - Father's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHMF - Mother's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHMM - Mother's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowRACYOU - Participant race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACFATH - Father's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACMOTH - Mother's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACFF - Father's Father's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACFM - Father's Mother's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACMF - Mother's Father's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACMM - Mother's Mother's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't know
0AC000003AL10061181214/9/071.02.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
1AC000005AL10038812210/18/061.02.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
2AC000006NaN118210/23/07NaN1.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
3AC000007NaN80703/17/08NaN1.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
4AC000009NaN783254/4/074.02.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
5AC000010NaN12510/24/06NaN1.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
6AC000011NaN797312/14/061.02.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
7AC000012NaN815611/28/07NaN1.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
8AC000013NaN782269/20/061.02.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
9AC000014AL10016214211/21/061.02.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0

Last rows

BARCODE2BARCODE2_V2SITE_IDPATIENT_IDDTBLDR Date of blood drawDISEASE2 1=MS 2=NMO 3=ON 4=TM 5=ADEM 6=CIS blank = controlDIAGDIS2 1=Control 2=CaseVISIT_IDVISIT_ID.1ROW_NOETHYOU - Participant ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHFATH - Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHMOTH - Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHFF - Father's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHFM - Father's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHMF - Mother's Father's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowETHMM - Mother's Mother's ethnicity 1=Hispanic or Latino 2=Non Hispanic or Latino 3=Don't knowRACYOU - Participant race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACFATH - Father's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACMOTH - Mother's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACFF - Father's Father's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACFM - Father's Mother's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACMF - Mother's Father's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't knowRACMM - Mother's Mother's race 1=American Indian or Alaska native 2=Middle Eastern 3=South Asian 4=Other Asian 5=Black or African American 6=Native Hawaiian or other Pacific Islander 7=White 8=Don't know
3069PC001032NaN789266/15/111.02.00003.03.03.03.03.03.03.07.07.07.07.07.07.07.0
3070PC001037NaN823987/11/112.02.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
3071PC001038NaN789532/13/122.02.00001.01.01.01.01.01.01.08.08.08.08.08.08.08.0
3072PC001042NaN8243311/9/112.02.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
3073PC001058NaN789584/2/122.02.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
3074PC001059NaN8255210/23/12NaN1.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
3075PC001064NaN8255010/23/12NaN1.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
3076PC001065NaN8255810/23/12NaN1.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
3077PC001069NaN161312/12/12NaN1.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0
3078PC001070NaN8264410/23/12NaN1.00002.02.02.02.02.02.02.07.07.07.07.07.07.07.0